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Highlights Behavioral and EEG effects of multifocal frontoparietal tDCS are investigated in patients with severe brain injury. No behavioral treatment effect was identified at the group level while EEG complexity increased in low frequency bands. Electrophysiological changes were not translated into behavioral changes at the group level.

Abstract. Studying emotions has become increasingly popular in various research fields. Researchers across the globe have studied various tools to implicitly assess emotions and affective states of people. Human computer interface systems specifically can benefit from such implicit emotion evaluator module, which can help them determine their users' affective states and act accordingly. Brain electrical activity can be considered as an appropriate candidate for extracting emotion-related cues, but it is still in its infancy. In this paper, the results of analyzing the Electroencephalogram (EEG) for assessing emotions elicited during watching various pre-selected emotional music video clips have been reported. More precisely, in-depth results of both subject-dependent and subjectindependent correlation analysis between time domain, and frequency domain features of EEG signal and subjects' self assessed emotions are produced and discussed.

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Idiopathic rapid eye movement sleep behavior disorder (RBD) is a serious risk factor for neurodegenerative processes such as Parkinson's disease (PD). We investigate the use of EEG algorithmic complexity derived metrics for its prognosis. We analyzed resting state EEG data collected from 114 idiopathic RBD patients and 83 healthy controls in a longitudinal study forming a cohort in which several RBD patients developed PD or dementia with Lewy bodies. Multichannel data from ~ 3 min recordings was converted to spectrograms and their algorithmic complexity estimated using Lempel-Ziv-Welch compression. Complexity measures and entropy rate displayed statistically significant differences between groups. Results are compared to those using the ratio of slow to fast frequency power, which they are seen to complement by displaying increased sensitivity even when using a few EEG channels. Poor prognosis in RBD appears to be associated with decreased complexity of EEG spectrograms stemming in part from frequency power imbalances and cross-frequency amplitude algorithmic coupling. Algorithmic complexity metrics provide a robust, powerful and complementary way to quantify the dynamics of EEG signals in RBD with links to emerging theories of brain function stemming from algorithmic information theory.